The examination of a cervical smear by what often is referred to as a Pap test is a mass screening cytological examination which currently requires individual visual inspection by a person of virtually all of the approximately 100,000 cells on a typical slide. The test, therefore, suffers from a high false negative rate due to the tedium and fatigue associated with this requirement for exhaustive search.
Prompted by the clear commercial potential for automated cervical smear analysis, several attempts to this end have been made heretofore. These attempts have proven to be unsuccessful at least partly because they could not accommodate overlapping cells as are typically found in the Pap smear. To circumvent the classification problems created by overlapping cells, specialized "monolayer preparations" have been prepared. A monolayer preparation is a specially prepared smear in which the cervical cells are centrifuged and filtered so that only a single layer of cells results. Besides serious cell preservation and cell transportation problems, the expense and time involved in the monolayer preparation precludes its use as a population screening substitute for the Pap smear.
Even when limited to the non-overlapping cell images provided by the monolayer preparation, prior art attempts at automated cytological classification have not been able to process cervical smear images at anything close to manual processing time. Many of these attempts at automated cytological classification have relied on feature extraction algorithms which attempt to select and to measure some feature within the image, e.g., the shape of the cell necleus. Feature extraction algorithms have failed due to the inability to segment the image into the components which require measurement. One cannot measure nuclear size, for example, unless the image is segmented so that the cellular nuclei are identified. Template matching, in which an actual image (not a mathematical quantity) is compared with stored exemplar images also has not been successful since it is computationally intensive and the infinite variety of possible Pap smear images or scenes would require an excessive number of exemplar image comparisons. The distinction between feature extraction and template matching is outlined in the Collings reference on pages 1 through 5 while image segmentation techniques are discussed in Chapter 7 of the Gonzalez reference.
An example of the limitations of the prior art can be found in the 1987 reference entitled "Automated Cervical Screen Classification" by Tien et al, identified further below.
Background references of interest are, as follows:
Rumelhart, David E. and McClelland, James L., "Parallel Distributed Processing," MIT Press, 1986, Volume 1;
Tien, D. et al, "Automated Cervical Smear Classification," Proceedings of the IEEE/Ninth Annual Conference of the Engineering in Medicine and Biology Society, 1987, p. 1457-1458;
Hecht-Nielsen, Robert, "Neurocomputing: Picking the Human Brain," IEEE Spectrum, March, 1988, p. 36-41; and
Lippmann, Richard P., "An Introduction to Computing with Neural Nets," IEEE ASSP Magazine, April, 1987, p. 4-22.